arXiv subjects

Yiying Zhang

Publications and source records attributed to Yiying Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

On multivariate contribution measures of systemic risk with applications in cryptocurrency market

Conditional risk measures and their associated risk contribution measures are commonly employed in finance and actuarial science for evaluating systemic risk and quantifying the effects of risk interactions. This paper introduces various types of contribution ratio measures based on the MCoVaR, MCoES, and MMME studied in Ortega-Jiménez et al. (2021) and Das & Fasen-Hartmann (2018) to assess the relative effects of a single risk when other risks in a group are in distress. The properties of these contribution risk measures are examined, and sufficient conditions for comparing these measures between two sets of random vectors are established using univariate and multivariate stochastic orders and statistically dependent notions. Numerical examples are presented to validate these conditions. Finally, a real dataset from the cryptocurrency market is used to analyze the spillover effects through our proposed contribution measures.

q-fin.RM↗

arXiv2025

Self-protection and insurance demand with convex premium principles

In economic analysis, rational decision-makers often take actions to reduce their risk exposure. These actions include purchasing market insurance and implementing prevention measures to modify the shape of the loss distribution. Under the assumption that the insureds' actions are fully observed by the insurer, this paper investigates the interaction between self-protection and insurance demand when insurance premiums are determined by convex premium principles within the framework of distortion risk measures. Specifically, the insured selects an optimal proportional insurance share and prevention effort to minimize the risk measure of their end-of-period exposure. We explicitly characterize the optimal combination of prevention effort and insurance demand in a self-protection model when the insured adopts tail value-at-risk or a subclass with strictly concave distortion functions. Additionally, we conduct comparative static analyses to illustrate our main findings under various premium structures, risk aversion levels, and loss distributions. Our results indicate that market insurance and self-protection are complementary, supporting classical insights from the literature regarding corner insurance policies (i.e., null and full insurance) in the absence of ex ante moral hazard. Finally, we consider the effects of moral hazard on the interaction between self-protection and insurance demand. Our findings show that ex ante moral hazard shifts the complementary effect into substitution effect.

q-fin.RM↗

arXiv2025

Cognify: Supercharging Gen-AI Workflows With Hierarchical Autotuning

Today's gen-AI workflows that involve multiple ML model calls, tool/API calls, data retrieval, or generic code execution are often tuned manually in an ad-hoc way that is both time-consuming and error-prone. In this paper, we propose a systematic approach for automatically tuning gen-AI workflows. Our key insight is that gen-AI workflows can benefit from structure, operator, and prompt changes, but unique properties of gen-AI workflows require new optimization techniques. We propose AdaSeek, an adaptive hierarchical search algorithm for autotuning gen-AI workflows. AdaSeek organizes workflow tuning methods into different layers based on the user-specified total search budget and distributes the budget across different layers based on the complexity of each layer. During its hierarchical search, AdaSeek redistributes the search budget from less useful to more promising tuning configurations based on workflow-level evaluation results. We implement AdaSeek in a workflow autotuning framework called Cognify and evaluate Cognify using six types of workflows such as RAG-based QA and text-to-SQL transformation. Overall, Cognify improves these workflows' generation quality by up to 2.8x, reduces execution monetary cost by up to 10x, and reduces end-to-end latency by 2.7x.

cs.LG↗

arXiv2025

SC-Bench: A Large-Scale Dataset for Smart Contract Auditing

There is a huge demand to ensure the compliance of smart contracts listed on blockchain platforms to safety and economic standards. Today, manual efforts in the form of auditing are commonly used to achieve this goal. ML-based automated techniques have the promise to alleviate human efforts and the resulting monetary costs. However, unlike other domains where ML techniques have had huge successes, no systematic ML techniques have been proposed or applied to smart contract auditing. We present SC-Bench, the first dataset for automated smart-contract auditing research. SC-Bench consists of 5,377 real-world smart contracts running on Ethereum, a widely used blockchain platform, and 15,975 violations of standards on Ehereum called ERCs. Out of these violations, 139 are real violations programmers made. The remaining are errors we systematically injected to reflect the violations of different ERC rules. We evaluate SC-Bench using GPT-4 by prompting it with both the contracts and ERC rules. In addition, we manually identify each violated rule and the corresponding code site (i.e., oracle) and prompt GPT-4 with the information asking for a True-or-False question. Our results show that without the oracle, GPT-4 can only detect 0.9% violations, and with the oracle, it detects 22.9% violations. These results show the potential room for improvement in ML-based techniques for smart-contract auditing.

cs.CR↗

arXiv2024

On Vulnerability Conditional Risk Measures: Comparisons and Applications in Cryptocurrency Market

We introduce a novel class of systemic risk measures, the Vulnerability Conditional risk measures, which try to capture the "tail risk" of a risky position in scenarios where one or more market participants is experiencing financial distress. Various theoretical properties of Vulnerability Conditional risk measures, along with a series of related contribution measures, have been considered in this paper. We further introduce the backtesting procedures of VCoES and MCoES. Through numerical examples, we validate our theoretical insights and further apply our newly proposed risk measures to the empirical analysis of cryptocurrencies, demonstrating their practical relevance and utility in capturing systemic risk.

q-fin.RM↗

arXiv2024

Preble: Efficient Distributed Prompt Scheduling for LLM Serving

Prompts to large language models (LLMs) have evolved beyond simple user questions. For LLMs to solve complex problems, today's practices are to include domain-specific instructions, illustration of tool usages, and/or long context such as textbook chapters in prompts. As such, many parts of prompts are repetitive across requests. Recent works propose to cache and reuse KV state of prompts. However, they are all confined to a single-GPU optimization, while production LLM serving systems are distributed by nature. This paper proposes Preble, the first distributed LLM serving platform that targets and optimizes for prompt sharing. We designed a distributed scheduling system that co-optimizes KV state reuse and computation load-balancing with a new scheduling algorithm and a hierarchical scheduling mechanism. Our evaluation of Preble with real workloads and request arrival patterns on two open-source LLMs shows that Preble outperforms the SOTA serving systems by 1.5X to 14.5X on average latency and 2X to 10X on p99 latency.

cs.DC↗

arXiv2024

DRust: Language-Guided Distributed Shared Memory with Fine Granularity, Full Transparency, and Ultra Efficiency

Despite being a powerful concept, distributed shared memory (DSM) has not been made practical due to the extensive synchronization needed between servers to implement memory coherence. This paper shows a practical DSM implementation based on the insight that the ownership model embedded in programming languages such as Rust automatically constrains the order of read and write, providing opportunities for significantly simplifying the coherence implementation if the ownership semantics can be exposed to and leveraged by the runtime. This paper discusses the design and implementation of DistR, a Rust-based DSM system that outperforms the two state-of-the-art DSM systems GAM and Grappa by up to 2.64x and 29.16x in throughput, and scales much better with the number of servers.

cs.DC↗

arXiv2024

InferCept: Efficient Intercept Support for Augmented Large Language Model Inference

Large language models are increasingly integrated with external environments, tools, and agents like ChatGPT plugins to extend their capability beyond language-centric tasks. However, today's LLM inference systems are designed for standalone LLMs. They treat each external interaction as the end of LLM generation and form a new request when the interaction finishes, causing unnecessary recomputation of already computed contexts, which accounts for 37-40% of total model forwarding time. This paper presents InferCept, the first LLM inference framework targeting augmented LLMs and supporting the efficient interception of LLM generation. InferCept minimizes the GPU resource waste caused by LLM interceptions and dedicates saved memory for serving more requests. InferCept improves the overall serving throughput by 1.6x-2x and completes 2x more requests per second compared to the state-of-the-art LLM inference systems.

cs.LG↗

arXiv2024

On Joint Marginal Expected Shortfall and Associated Contribution Risk Measures

Systemic risk is the risk that a company- or industry-level risk could trigger a huge collapse of another or even the whole institution. Various systemic risk measures have been proposed in the literature to quantify the domino and (relative) spillover effects induced by systemic risks such as the well-known CoVaR, CoES, MES and CoD risk measures, and associated contribution measures. This paper proposes another new type of systemic risk measure, called the joint marginal expected shortfall (JMES), to measure whether the MES of one entity's risk-taking adds to another one or the overall risk conditioned on the event that the entity is already in some specified distress level. We further introduce two useful systemic risk contribution measures based on the difference function or relative ratio function of the JMES and the conventional ES, respectively. Some basic properties of these proposed measures are studied such as monotonicity, comonotonic additivity, non-identifiability and non-elicitability. For both risk measures and two different vectors of bivariate risks, we establish sufficient conditions imposed on copula structure, stress levels, and stochastic orders to compare these new measures. We further provide some numerical examples to illustrate our main findings. A real application in analyzing the risk contagion among several stock market indices is implemented to show the performances of our proposed measures compared with other commonly used measures including CoVaR, CoES, MES, and their associated contribution measures.

q-fin.RM↗

arXiv2024

Zenix: Efficient Execution of Bulky Serverless Applications

Serverless computing, commonly offered as Function-as-a-Service, was initially designed for small, lean applications. However, there has been an increasing desire to run larger, more complex applications (what we call bulky applications) in a serverless manner. Existing strategies for enabling such applications are to either increase function sizes or to rewrite applications as DAGs of functions. These approaches cause significant resource wastage, manual efforts, and/or performance overhead. We argue that the root cause of these issues is today's function-centric serverless model, where a function is the resource allocation and scaling unit. We propose a new, resource-centric serverless-computing model for executing bulky applications in a resource- and performance-efficient way, and we build the Zenix serverless platform following this model. Our results show that Zenix reduces resource consumption by up to 90% compared to today's function-centric serverless systems, while improving performance by up to 64%.

cs.DC↗

arXiv2024

AuditGPT: Auditing Smart Contracts with ChatGPT

To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each containing a set of rules to guide the behaviors of smart contracts. Violating the ERC rules could cause serious security issues and financial loss, signifying the importance of verifying smart contracts follow ERCs. Today's practices of such verification are to either manually audit each single contract or use expert-developed, limited-scope program-analysis tools, both of which are far from being effective in identifying ERC rule violations. This paper presents a tool named AuditGPT that leverages large language models (LLMs) to automatically and comprehensively verify ERC rules against smart contracts. To build AuditGPT, we first conduct an empirical study on 222 ERC rules specified in four popular ERCs to understand their content, their security impacts, their specification in natural language, and their implementation in Solidity. Guided by the study, we construct AuditGPT by separating the large, complex auditing process into small, manageable tasks and design prompts specialized for each ERC rule type to enhance LLMs' auditing performance. In the evaluation, AuditGPT successfully pinpoints 418 ERC rule violations and only reports 18 false positives, showcasing its effectiveness and accuracy. Moreover, AuditGPT beats an auditing service provided by security experts in effectiveness, accuracy, and cost, demonstrating its advancement over state-of-the-art smart-contract auditing practices.

cs.CR↗

arXiv2023

Magnetic kagome materials RETi3Bi4 family with weak interlayer interactions

Kagome materials have attracted a surge of research interest recently, especially for the ones combining with magnetism, and the ones with weak interlayer interactions which can fabricate thin devices. However, kagome materials combining both characters of magnetism and weak interlayer interactions are rare. Here we investigate a new family of titanium based kagome materials RETi3Bi4 (RE = Eu, Gd and Sm). The flakes of nanometer thickness of RETi3Bi4 can be obtained by exfoliation due to the weak interlayer interactions. According to magnetic measurements, out-of-plane ferromagnetism, out-of-plane anti-ferromagnetism, and in-plane ferromagnetism are formed for RE = Eu, Gd, and Sm respectively. The magnetic orders are simple and the saturation magnetizations can be relatively large since the rare earth elements solely provide the magnetic moments. Further by angle-resolved photoemission spectroscopy (ARPES) and first-principles calculations, the electronic structures of RETi3Bi4 are investigated. The ARPES results are consistent with the calculations, indicating the bands characteristic with kagome sublattice in RETi3Bi4. We expect these materials to be promising candidates for observation of the exotic magnetic topological phases and the related topological quantum transport studies.

cond-mat.mtrl-sci↗

arXiv2023

Generalized Location-Scale Mixtures of Elliptical Distributions: Definitions and Stochastic Comparisons

This paper proposes a unified class of generalized location-scale mixture of multivariate elliptical distributions and studies integral stochastic orderings of random vectors following such distributions. Given a random vector $\boldsymbol{Z}$, independent of $\boldsymbol{X}$ and $\boldsymbol{Y}$, the scale parameter of this class of distributions is mixed with a function $α(\boldsymbol{Z})$ and its skew parameter is mixed with another function $β(\boldsymbol{Z})$. Sufficient (and necessary) conditions are established for stochastically comparing different random vectors stemming from this class of distributions by means of several stochastic orders including the usual stochastic order, convex order, increasing convex order, supermodular order, and some related linear orders. Two insightful assumptions for the density generators of elliptical distributions, aiming to control the generators' tail, are provided to make stochastic comparisons among mixed-elliptical vectors. Some applications in applied probability and actuarial science are also provided as illustrations on the main findings.

math.ST↗

arXiv2022

SuperNIC: A Hardware-Based, Programmable, and Multi-Tenant SmartNIC

With CPU scaling slowing down in today's data centers, more functionalities are being offloaded from the CPU to auxiliary devices. One such device is the SmartNIC, which is being increasingly adopted in data centers. In today's cloud environment, VMs on the same server can each have their own network computation (or network tasks) or workflows of network tasks to offload to a SmartNIC. These network tasks can be dynamically added/removed as VMs come and go and can be shared across VMs. Such dynamism demands that a SmartNIC not only schedules and processes packets but also manages and executes offloaded network tasks for different users. Although software solutions like an OS exist for managing software-based network tasks, such software-based SmartNICs cannot keep up with the quickly increasing data-center network speed. This paper proposes a new SmartNIC platform called SuperNIC that allows multiple tenants to efficiently and safely offload FPGA-based network computation DAGs. For efficiency and scalability, our core idea is to group network tasks into chains that are connected and scheduled as one unit. We further propose techniques to automatically scale network task chains with different types of parallelism. Moreover, we propose a fair share mechanism that considers both fair space sharing and fair time sharing of different types of hardware resources. Our FPGA prototype of SuperNIC achieves high bandwidth, low latency performance whilst efficiently utilizing and fairly sharing resources.

cs.DC↗

arXiv2022

Canvas: Isolated and Adaptive Swapping for Multi-Applications on Remote Memory

Remote memory techniques for datacenter applications have recently gained a great deal of popularity. Existing remote memory techniques focus on the efficiency of a single application setting only. However, when multiple applications co-run on a remote-memory system, significant interference could occur, resulting in unexpected slowdowns even if the same amounts of physical resources are granted to each application. This slowdown stems from massive sharing in applications' swap data paths. Canvas is a redesigned swap system that fully isolates swap paths for remote-memory applications. Canvas allows each application to possess its dedicated swap partition, swap cache, prefetcher, and RDMA bandwidth. Swap isolation lays a foundation for adaptive optimization techniques based on each application's own access patterns and needs. We develop three such techniques: (1) adaptive swap entry allocation, (2) semantics-aware prefetching, and (3) two-dimensional RDMA scheduling. A thorough evaluation with a set of widely-deployed applications demonstrates that Canvas minimizes performance variation and dramatically reduces performance degradation.

cs.OS↗

arXiv2022

Exploiting Network Loss for Distributed Approximate Computing with NetApprox

Many data center applications such as machine learning and big data analytics can complete their analysis without processing the complete set of data. While extensive approximate-aware optimizations have been proposed at hardware, programming language, and application levels. However, to date, the approximate computing optimizations have ignored the network layer. We propose NetApprox, which to the best of our knowledge, is the first approximate-aware network layer comprising transport-layer protocol, network resource allocation schemes, and scheduling/priority-assignment policies. Building on the observation that approximate applications can tolerate loss, NetApprox's main insights are to aggressively send approximate traffic (which improves the performance of approximate applications) and to minimize the network resources allocated to approximate traffic (which simultaneously limits the impact of aggressive approximate traffic while freeing up resources that, in turn, improve non-approximate applications' performance). We ported Flink, Kafka, Spark, and PyTorch to NetApprox and evaluated NetApprox with both large-scale simulation and real implementation. Our evaluation results show that NetApprox improves job completion times by up to 80% compared to network-oblivious approximation solutions, and improves the performance of co-running non-approximate workloads by 79%.

cs.NI↗

arXiv2022

Protecting File Activities via Deception for ARM TrustZone

A TrustZone TEE often invokes an external filesystem. While filedata can be encrypted, the revealed file activities can leak secrets. To hide the file activities from the filesystem and its OS, we propose Enigma, a deception-based defense injecting sybil file activities as the cover of the actual file activities. Enigma contributes three new designs. (1) To make the deception credible, the TEE generates sybil calls by replaying file calls from the TEE code under protection. (2) To make sybil activities cheap, the TEE requests the OS to run K filesystem images simultaneously. Concealing the disk, the TEE backs only one image with the actual disk while backing other images by only storing their metadata. (3) To protect filesystem image identities, the TEE shuffles the images frequently, preventing the OS from observing any image for long. Enigma works with unmodified filesystems shipped withLinux. On a low-cost Arm SoC with EXT4 and F2FS, our system can concurrently run as many as 50 filesystem images with 1% of disk overhead per additional image. Compared to common obfuscation for hiding addresses in a flat space, Enigma hides file activities with richer semantics. Its cost is lower by one order of magnitude while achieving the same level of probabilistic security guarantees.

cs.CR↗

arXiv2022

Tunable Majorana corner modes by orbital-dependent exchange interaction in a two-dimensional topological superconductor

We theoretically study the effect of orbital-dependent exchange field in the formation of second order topological superconductors. We demonstrate that changing the orbital difference can induce topological transition and the Majorana corner modes therein can be manipulated. We further propose to detect the corner modes via a normal probe terminal. The conductance quantization is found to be robust to changes of the relevant system parameters.

cond-mat.supr-con↗